AI-Driven Cyber Threat Detection and Mitigation in 5G and Beyond: Enhancing Security in the Telecom Industry - A Survey and Comparative Analysis

Abstract

The rise of 5G and Beyond-5G (B5G) networks has revolutionized the telecom industry, enabling high- speed, low-latency communication and massive IoT connectivity. However, these advancements have introduced complex and evolving cyber threats. Traditional security systems are no longer sufficient to defend against zero-day attacks, adversarial manipulations, and sophisticated intrusions. This pa- per provides a comprehensive survey and comparative analysis of AI-driven cyber threat detection and mitigation techniques in 5G/B5G networks, covering machine learning (ML), deep learning (DL), reinforcement learning (RL), and hybrid AI approaches. A layered AI-security architecture is proposed, and each method is evaluated across multiple dimensions such as accuracy, scalability, real-time feasibility, and computational complexity. The study also highlights future directions, including edge AI, federated learning, explainable AI (XAI), and quantum AI, offering a roadmap for secure and intelligent next-generation networks

Introduction

The deployment of 5G and Beyond 5G (B5G) networks supports advanced applications like smart cities, autonomous vehicles, and industrial automation but also expands cyberattack surfaces, challenging traditional security methods. Artificial Intelligence (AI), including Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL), offers powerful tools for threat detection, anomaly identification, and automated response in telecom cybersecurity.

An AI-enhanced security architecture for 5G/B5G typically includes layers for data collection, preprocessing, detection, response, and continuous learning. Leading vendors deploy edge AI and federated learning to detect threats locally while preserving privacy.

Key AI techniques used include:

• ML: Supervised, unsupervised, and semi-supervised learning for classifying known attacks and detecting anomalies.

• DL: CNNs and RNNs for complex pattern recognition, and GANs to improve robustness.

• RL: Real-time adaptive defense strategies like Deep Q-Learning and multi-agent coordination.

Challenges include data privacy concerns, adversarial AI attacks, computational demands, real-time latency requirements, and interoperability issues.

Future trends focus on edge AI deployment, federated learning for privacy, explainable AI for transparency, quantum AI for enhanced processing, and digital twins to simulate cyberattacks.

AI-powered applications in telecom range from intrusion detection, fraud prevention, encrypted traffic analysis, self-healing networks, to network slicing security. Examples include Reliance Jio’s AI-driven network optimization and Airtel’s AI-enabled home broadband security.

Conclusion

AI is transforming cybersecurity in 5G and B5G telecom networks by enabling intelligent threat detection and real-time response. This paper surveyed ML, DL, and RL techniques, proposed a layered AI-based security architecture, and highlighted real-world telecom use cases. While challenges like data privacy, adversarial attacks, and latency persist, emerging trends such as Edge AI, Federated Learning, and Explainable AI show great potential. Moving forward, robust and interpretable AI solutions will be key to building secure and resilient telecom infrastructures.

References

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Copyright

Copyright © 2025 Kshitij Nirdhar , Amit Hatekar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.